Various adaptive filter structures have been developed for use in time updated adaptive systems to solve acoustical echo cancellation, channel equalization and other problems; examples of such structures include for example, transversal, multistage lattice, systolic array, and recursive implementations. Among these, transversal finite-impulse-response (FIR) filters are often used, due to stability considerations, and to their versatility and ease of implementation. Many algorithms have also been developed to adapt these filters, including the least-mean-squares (LMS), recursive least-squares, sequential regression, and least-squares lattice algorithms.
A deficiency of many existing methods is that they generate new sets of filter coefficients iteratively, where the next set of filter coefficients depends on the previous set of filter coefficients. This is the case for the commonly used least-mean-square (LMS) algorithm. Occasionally, a newly generated set of filter coefficients is a worse representation of the impulse response to be approximated than the previously generated set of filter coefficients. However, existing methods generally select the new set of filter coefficients and provide no suitable method for addressing this deficiency.
Consequently, there is a need in the industry for providing filter adaptation unit suitable for producing a set of filter coefficients that alleviates, at least in part, the deficiencies of the prior art.